This is what happened when Beelzebub the Cat decided to try to play my saxophone after I had foolishly left it on its stand without its mouthpiece cap.
He seriously needs to work on his embouchure.
I seriously need to disinfect my mouthpiece.
bookmarks about learning and motivation
bookmarks relating to motivation
Educational technology bookmarks
Dave Cormier, in typically excellent form, reflects on the differences between education and learning in his latest post. I very much agree with pretty much everything he writes here. This extract condenses the central point that, I think, matters more than any other:
“Learning is a constant. It is what humans do. They don’t, ever, learn exactly what you want them to learn in your education system. They may learn to remember that 7+5=12 as my children are currently being taught to do by rote, but they also ‘learn’ that math is really boring. We drive them to memorise so their tests will be higher, but is it worth the tradeoff? Is a high score on addition worth “math is boring?””
This is crucial: it is impossible to live and not to learn. Failure to learn is not an option. What matters is what we learn and how we learn it. The thing is, as Dave puts it:
“Education is a totally different beast than learning. Learning is a thing a person does. Education is something a society does to its citizens. When we think about what we want to do with ‘education’ suddenly we need to start thinking about what we as a society think is important for our citizens to know. There was a time, in an previous democracy, where learning how to interact in your democracy was the most important part of an education system. When i look through my twitter account now I start to think that learning to live and thrive with difference without hate and fear might be a nice thing for an education system to be for.”
I have written here and there about the deep intertwingled relationship between education and indoctrination (e.g, most recently, here). Most of its early formal incarnations were, and a majority of them still are, concerned with passing on doctrine, often of a religious, quasi-religious, or political nature. To do that also requires the inculcation of values, and the acquisition of literacies (by my definition, the set of hard, human-enacted technologies needed to engage with a given culture, be that culture big or small). The balance between indoctrination, inculcation and literacy acquisition has shifted over the years and varies according to culture, context, and level, but education remains, at its heart, a process for helping learners learn to be in a given society or subset of it. This remains true even at the highest levels of terminal degrees: PhDs are almost never about the research topic so much as they are about learning to be an academic, a researcher, someone that understands and lives the norms, values and beliefs of the academic research community in which their discipline resides. To speak the language of a discipline. It is best to speak multiple languages, of course. One of the reasons I am a huge fan of crossing disciplinary boundaries is that it slightly disrupts that process by letting us compare, contrast, and pick between the values of different cultures, but such blurring is usually relatively minor. Hard core physicists share much in common with the softest literary theorists. Much has been written about the quality of ‘graduateness‘, typically with some further intent in mind (eg. employability) but what the term really refers to is a gestalt of ways of thinking, behaving, and believing that have what Wittgenstein thought of as family likenesses. No single thing or cluster of things typifies a graduate, but there are common features spread between them. We are all part of the same family.
Education has a lot to do with replication and stability but it is, and must always have been, at least as much about being able to adapt and change that society. While, in days gone by, it might have been enough to use education as a means to produce submissive workers, soldiers, and priests, and to leave it to higher echelons to manage change (and manage their underlings), it would be nice to think that we have gone beyond that now. In fact, we must go beyond that now, if we are to survive as a species and as a planet. Our world is too complex for hierarchical management alone.
I believe that education must be both replicative and generative. It must valorize challenge to beliefs and diversity as much as it preserves wisdom and uniformity. It must support both individual needs and social needs, the needs of people and the needs of the planet, the needs of all the societies within and intersecting with its society. This balance between order and chaos is about sustaining evolution. Evolution happens on the edge of chaos, not in chaos itself (the Red Queen Regime), and not in order (the Stalinist Regime). This is not about design so much as it is about the rules of change in a diverse complex adaptive system. The ever burgeoning adjacent possible means that our societies, as much as ecosystems, can do nothing but evolve to ever greater complexity, ever greater interdependence but, equally, ever greater independence, ever greater diversity. We are not just one global society, we are billions of them, overlapping, cross-cutting, independent, interdependent. And there is not just one educational system that needs to change. There are millions of them, millions of pieces of them, and more of them arriving all the time. We don’t need to change Education: that’s too simplistic and would, inevitably, just replace one set of mistakes with another. We need to change educations.
Address of the bookmark: http://davecormier.com/edblog/2016/10/24/planning-for-educational-change-what-is-education-for/
Delightful compendium from Bryan Alexander. I particularly like:
Analytics, n. pl. “The use of numbers to confirm existing prejudices, and the design of complex systems to generate these numbers.”
Big data. n. pl. 1.When ordinary surveillance just isn’t enough.
Failure, n. 1. A temporary practice educators encourage in students, which schools then ruthlessly, publicly, and permanently punish.
Forum, n. 1. Social Darwinism using 1980s technology.
World Wide Web, n. A strange new technology, the reality of which can be fended off or ignored through the LMS, proprietary databases, non-linking mobile apps, and judicious use of login requirements.
This looks really excellent – it scrapes Google Scholar, starting with a search that reveals work you already know about and that you think is significant. From those search results it generates an exportable Gephi map of authors, subject/disciplinary areas and links between them. Basically, it automatically (well – a little effort and a bit of Google Scholar/Gephi competence needed) maps out connected research areas and authors, mined from Google Scholar, including their relative significance and centrality, shaped to fit your research interests. Doing this manually, as most researchers do, takes a really long time, and it is incredibly easy to miss significant authors and connections. This looks like a fantastic way to help build a literature review, and great scaffolding to help with exploring a research area. I see endless possibilities and uses. Of course, it is only as good as the original query, and only as good as Google Scholar’s citation trail, but that’s an extremely good start, and it could be iterated many times to refine the results further. The code for the tool, Bibnet, is available through Github.
This is great fun and quite fascinating – do try it out. You get to click on a rectangle, then see where other people have clicked – many thousands of them.
This system is incredibly similar to part of an experiment on collective social navigation behaviour that I performed over ten years ago, albeit mine was at a much smaller scale and graphically a little coarser, and I deliberately asked people to click where they thought most other people would click. What’s interesting is that, though I only had a couple of hundred participants overall, and only just over a hundred got this view, the heat map of this new system is almost exactly the same shape as mine, though the nuances are more defined here thanks to the large numbers involved.
In my experiment (the paper was called ‘On the stupidity of mobs’) this was the control case: the other subjects got to see where others in their group had previously clicked. They did not see the clicks of the control group and did not know how later subjects might behave, so finding the most popular point was not as trivial as it sounds. I was expecting stupid behaviour in those that could see where others had clicked but it was not quite so simple. It appeared that people reacted in three distinctly different ways to seeing the clicks of others. About a third followed the herd (as anticipated) and about a third deliberately avoided the herd (not expected). About a third continued to make reasoned decisions, apparently uninfluenced by others, much as those without such cues. Again, I had not expected this. I should have expected it, of course. Similar issues were well known in the context of weighted lists such as Google Search results or reviews on Amazon, where some users deliberately seek less highly rated items or ignore list order in an attempt to counter perceived bias, and I had seen – but not well understood – similar effects in earlier case studies with other more practically oriented social navigation systems. People are pretty diverse! I wonder whether the researchers here are aiming for something similar? It does offer the opportunity to try again later (not immediately) so they could in theory analyze the results of the influence of others in a similar way. I’d love to see those results.
Address of the bookmark: http://boltkey.cz/multiclick/
Alfie Kohn in brilliant form once again, reaffirming his place as the most eloquent writer on motivation this century, this time taking on the ‘bonus effect’ – the idea that giving rewards makes those rewards themselves more desirable while simultaneously devaluing the activity leading to them. It seems that, though early research was equivocal, more recent studies show that this is real:
“When people are promised a monetary reward for doing a task well, the primary outcome is that they get more excited about money. This happens even when they don’t meet the standard for getting paid. And when a reward other than money is used — raffle tickets for a gift box, in this case — the effect is the same: more enthusiasm about what was used as an incentive.”
“The more closely a reward is conditioned on how well one has done something, the more that people come to desire the reward and, as earlier research has shown, the more they tend to lose interest in whatever they had to do to get the reward.”
As Kohn summarizes:
‘If the question is “Do rewards motivate people?” the answer is “Sure — they motivate people to get rewards.”’
We have long known that performance-related pay is a terrible idea, and that performance-related bonuses achieve the precise opposite of their intended effects. This is a great explanation of more of the reasons behind that empirical finding.
As it happens, Athabasca University operates just such a system, flying in the face of five decades of research that shows unequivocally that it is positively self-defeating. It’s bad enough when used to drive workers on a production line. For creative and problem-solving work, it is beyond ridiculous. Of course, as Kohn notes, exactly the same dynamic underlies most of our teaching too:
“If we try to justify certain instructional approaches by saying they’ll raise test scores, we’re devaluing those approaches while simultaneously elevating the importance of test scores. The same is true of education research that uses test results as the dependent variable.”
The revolution cannot come soon enough.
Address of the bookmark: http://www.alfiekohn.org/blogs/bonus/
A new ACM journal, Transactions on Social Computing. The scope seems good, with strong interests expressed not just in the computational side but the human and social side of the field. It will be interesting to see how this develops. Too much of the field is dominated by variations on either a theme of hard computing, especially social network analysis and filtering algorithms, or soft, expansive, but technologically underinformed studies. Both extremes matter, but the important stuff is found at their intersection, and neither is of much interest on its own. This looks like it might hit the sweet spot.
Address of the bookmark: http://tsc.acm.org/about.cfm